the effects of quantitative easing announcements on the

1

The Effects of Quantitative Easing Announcements on the Mortgage Market:

An Event Study Approach

Gang Wang

Department of Business Economics

The College of Wooster

206 Morgan Hall, 930 College Mall, Wooster, Ohio 44691, USA

Abstract

This paper uses event study analysis to estimate the impact of the Fed’s Quantitative Easing (QE)

announcements on the mortgage market during zero lower bound period. A total of 35 QE

announcements are identified and their effects are evaluated. The best-fitting IGARCH model with

skewed t distribution is used to measure the QE announcement effects on daily changes of the 30-

year mortgage rate, the 30-year Treasury rate and the spread between them. Announcements

suggesting the start of a new round of QE reduced the mortgage rate tremendously, while the

effects of further news diminished. Announcements of an increase in mortgage-backed security

purchases decreased the mortgage rate more than the Treasury rate and reduced the credit risk of

holding mortgage securities over Treasury securities. The long run effects of QE announcements

on the mortgage rate were less than short run effects but persistent. We also find that the previous

literature overestimate QE effects on interest rates in general.

Key Words: Event Studies, the Fed, GARCH, Monetary Policy, Mortgage, Quantitative Easing

JEL classification: E52, E58, G21

Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 9 November 2018 doi:10.20944/preprints201811.0243.v1

© 2018 by the author(s). Distributed under a Creative Commons CC BY license.

Peer-reviewed version available at Int. J. Financial Stud. 2019, 7, 9; doi:10.3390/ijfs7010009

http://dx.doi.org/10.20944/preprints201811.0243.v1

http://creativecommons.org/licenses/by/4.0/

http://dx.doi.org/10.3390/ijfs7010009

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1. Introduction

Unconventional monetary policy instruments have been widely employed by central banks

in major developed economies (i.e., U.S., U.K., Euro Area and Japan) since the 2007-2008

financial crisis. Among these instruments, Quantitative Easing (QE) was most widely used by

central banks and discussed by researchers. In the U.S., during the zero lower bound (ZLB) period1,

the Federal Reserve Bank (Fed) frequently implemented several rounds of QE such as Large-scale

Asset Purchase (LSAP) and Operation Twists2 (OT). Although the types and quantities of assets

purchased by the Fed were not the same during each round, the aim for the Fed’s QEs was that by

increasing the prices and decreasing the yields of government and agency assets through Fed’s

purchases, investors were more willing to buy private assets. As a result, better liquidity and less

credit constraints were achieved in the market. Mortgage Backed Securities (MBSs) were among

the securities purchased by the Fed and thus the yields of MBSs and mortgage rates were expected

to be reduced in the course of QEs. Former Chairman of the Fed Ben Bernanke said in his Jackson

Hole Speech on August 31, 2012: “QE program… has been linked to substantial reductions in

MBS yields and retail mortgage rates”.

While QE effects on asset prices in general are broadly studied, few researchers (Hancock,

Passmore, 2011, 2012 and 2015) have looked into the mortgage market. In the broad QE literature,

the VAR model is most commonly used to estimate the co-movement of mortgage rates and other

asset yields, but the effects of QE announcements on mortgage rates on event day or in an event

window have not been investigated until this paper.

The purpose of this paper is to analyze the QE announcement effects on the mortgage rate

1 ZLB period started at the end of 2008 when Fed reduced the federal funds rate to be in the range of 0 to 0.25

percent, and concluded at late 2015 when Fed decided to increase the federal funds rate to be in the range of 0.25 to

0.5 percent. 2 Also known as Maturity Extension Program (or MEP).





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and the spread over the Treasury rate. Different from Hancock and Passmore (2011, 2012, 2015)

who treat the announcements in the same round of QE as the same event, we consider all 35

announcements as different events and evaluate their effects on the mortgage rate respectively.

In general, we find that announcements of an increase in MBS purchases decreased the

mortgage rate more than the Treasury rate and narrowed the spread between the mortgage rate and

the Treasury rate. This is consistent with the finding in Di Maggio et al. (2016): Mortgage rates

decreased more in QE1 than in QE2 since the Fed only purchased Treasury bonds in QE2.

Our analysis has several advantages compared to the prior literature. Our data set are

updated to the end of 2015 which includes all the Fed’s QE announcement events during the ZLB

period. Using regression-based event studies to account for the effects reduces concerns regarding

endogeneity and overlapping event windows. We use the GARCH model to control for the serial

correlation and heteroskedasticity within the data series for better estimating the pure effect of

events. We summarize primarily formal methods and econometric evidence in QE announcement

effect literature and compare their results with ours. We find the QE announcement effects on the

mortgage rate in short run (i.e., on event day and in event windows) and in long run (i.e., assume

a steady state). Finally, we categorize the QE announcements by type and summarize their distinct

effects.

The outline of the paper is as follows: Section 1 gives the background and introduction.

Section 2 presents the literature review. Section 3 describes the data sources and descriptive

statistics. Section 4 shows the event study methodology and model selection. Section 5 discusses

the results. Section 6 analyzes the case when QE events are grouped by the type of announcements

and the round of QE. Section 7 demonstrates two robustness checks and Section 8 concludes the

findings.





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2. Literature review

2.1. QE announcement effects on interest rates and asset prices

A large amount of literature focuses on estimating the direct effects of QE announcements

on long-term interest rates and term premia. Wright (2012) finds that although QE shocks had

effects on both long-term interest rates and corporate bond yields, the effect decayed really fast.

Jarrow and Li (2012) evaluate the effects of the Fed’s QE1 and QE2 on US term premia of interest

rates. Li and Wei (2012) conclude that QE1, QE2 and OT combined result in a decrease of about

100 basis points on the 10-year treasury yield.

Hancock and Passmore (2011) evaluate effects of the Fed’s MBS purchase program in

2008 (part of QE1) on mortgage rates and MBS yields. They run linear regression of mortgage

rates on the determinants and period dummies to conclude that the program lowered the mortgage

rates. Hollifield (2011) points out two drawbacks in the research of Hancock and Passmore (2011),

which are a nonlinear relationship between MBS yields and their determinants, and endogenous

right-hand side regression variables to the Fed’s MBS purchase program. Hancock and Passmore

(2012) extend the data to include QE2 and OT, modify the determination models of mortgage rates

and MBS yields, and model the relation between these two variables. Hancock and Passmore (2015)

use a co-integrated, error- correction model to estimate the “stock” and “flow” effect3 of Fed’s

LSAP on MBS yields and mortgage rates. Different from their previous researches, they account

for the separate QE rounds (QE1, QE2, OT and QE3) by defining a dummy for each round. They

conclude that “portfolio rebalancing” channel is a more important consideration for QE

transmission than other channels. They also indicate that the “stock” effect dominates the “flow”

3 The stock effect means the effect of increases in the Fed’s asset holdings, while the flow effect means the effect of

QE announcements.





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effect of the Fed’s QE on MBS yields and mortgage rates.

Di Maggio et al. (2016) use micro-level mortgage market data to analyze the interest rate

movements and the origination volumes of assets in different rounds of QE. They find that the

interest rates and origination volumes depend on the segmentation of the market and the types of

assets purchased.

2.2. QE announcement effects analysis using event studies

A few researchers incorporate event studies4 to analyze QE effects. Swanson (2011) uses

event study to examine the QE announcement effects on Treasury yields during “Operation Twist”

in the 1960’s and QE2. Glick and Leduc (2012) consider the first principal component of yield

changes of 2-, 5-, 10- and 30- year U.S. bond futures in a 2-hour window (Wright, 2012) as the

Fed’s QE announcement shock and employ event studies to analyze QE announcement effects on

financial market. Patrabansh et al. (2014) use event study method to show how the 10-year

Treasury yield responded to the Fed’s QE announcement. Kozicki, Santor and Suchanek (2015)

incorporate event studies with GARCH (1,1) model to analyze the Fed’s LSAP announcement

effects on commodity prices and international spillovers. They find that LSAP announcements did

not lead to higher commodity prices in general but appreciated the commodity exporters’

currencies and brought gains to their stock markets.

3. Data

We analyze the 30-year fixed mortgage rate (FRM)5as the indicator for cost of financing a

single-family house. The corresponding benchmark- 30-year Treasury rate is also evaluated.

4 Event study literature and methodology are discussed in Appendix, Part 1. 5 Specifically, the mortgage rate is the daily overnight 30-year US home mortgage national average from Bankrate.





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Figure 1 shows these two data series along with the daily Freddie Mac 30-year current coupon

yield from January 3, 2000 to December 31, 2015. To eliminate the federal funds rate effect6 on

the mortgage rate, we only use data from 2, 2008 to December 31, 2015 which covers the whole

Zero Lower Bound (ZLB) period.

Before the initiation of QE, all three yields stayed in high levels. Then they all tumbled

during the initiation of QE1, QE2 and OT, rallied to relatively high levels when OT ended, and

were gradually declining in QE tapering. During each round of QE, the rates dropped sharply when

purchase programs were announced. There is a clear evidence that QE announcements had

influences on long term interest rates and spreads. The summary statistics are reported in Table 1.

4. Methodology

4.1. QE announcement dates for event study

The QE announcement events take several forms including announcements after FOMC

meetings, Fed testimonies, Fed Chairman’s speeches, Press Conference Reports and Fed minutes

released. An announcement is identified as a QE announcement based on two criteria. First, the

announcement should mention the QE program, either an indication of launching a new round of

QE or the types and quantities of assets the Fed planned to purchase. Second, the announcement

should contain news to the market other than mentioning the same thing as the last QE

announcement did.

Without the official version of QE announcement events timeline published by the Fed, we

identify the events from previous literature. There is a consensus among previous researchers

(Gagnon et al., 2011; Woodford, 2012; Hancock and Passmore, 2015; Glick and Leduc, 2015;

6 It is known as the effect of traditional monetary policy.





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Hattori et al., 2016; Altavilla and Giannone, 2017) that there were a total of 13 QE announcements

during QE1 and QE2. We include all 13 events during QE1 and QE2 in this paper. For OT (a.k.a.

MEP) period, we identify three events mentioned in Bowman et al. (2015) and Borrallo et al.

(2015), and one event mentioned in Hancock and Passmore (2015). Among these four events, two

hinted the possible OT program and the other two were official announcements of launching OT.

For QE3, we combine the events mentioned in Bowman et al. (2015) and Hancock and Passmore

(2015) and delete one “irrelevant” event7 to a total of four events. For QE tapering period, the first

four events are taken from Altavilla and Giannone (2017), three of which indicated the decreasing

pace of asset purchases and one was the official announcement of tapering. We update the data to

include another 10 events concerning stepwise QE tapering procedure until the end of QE program

on October 29, 2014. Finally, a total of 35 events is identified and reported in Table 2.

4.2. Event window, OLS issues and GARCH

To measure the effect of QE announcements on the mortgage rate using event studies, we

choose three different event window sizes (i.e., 1-day, 3-day and 5-day)8 for each of the 35 events

and run regressions according to each window size. Specifically, 1-day window only identifies the

event day on which there was a QE announcement; 3-day window consists of one pre-event day,

the event day and one post-event day; 5-day window is comprised of two pre-event days, the event

day and two post-event days. The augmented Dickey-Fuller unit root tests9 on levels and changes

of variables (i.e., 30-year mortgage rate, 30-year Treasury rate and the spread between these two)

7 The announcement on March 20, 2013 is considered irrelevant to QE since it only remarked the improved

economic and labor market conditions. It was treated as an unconventional monetary policy announcement (i.e.,

forward guidance) in Bowman et al. (2015), but should not be regarded as a QE announcement here. 8 Event windows larger than five days are not considered in my study to avoid the effects of other news. 9 The test results can be found in Table A1 in Appendix.





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validate that the changes of these variables are covariance stationary and not over differenced.

Instead of finding the abnormal return (AR) as the difference between the observed and

predicted return in traditional way with non-overlapping event windows, we use regression-based

event study methodology to allow for overlapping event windows10. The coefficient of the event

dummy corresponding to event k on day t is the abnormal return11 ( ktAR ) of the left hand side

variable. We run four different regressions and adjust for three different event window sizes (5-

day, 3-day and 1-day). The four regressions are

1,0 1,1 1 1,'t t t tMR TR D = + + + , (1)

2,0 2 2,'t t tMR D = + + , (2)

3,0 3 3,' = + +t t tTR D , (3)

4,0 4 4,'t t tSpread D = + + , (4)

where tMR is the change of 30-year mortgage rate from day t-1 to day t, tTR is the change of

30-year Treasury rate from day t-1 to day t, and tSpread is the change of spread between 30-year

mortgage rate and Treasury rate from day t-1 to day t. The Spread measures the perceived riskiness

of holding mortgages over Treasury bonds (i.e., the risk premium). tD is a 1L column vector of

event dummies taking value 1 on that day and 0 on other days. L equals to 35 for 1-day window,

105 for 3-day window and 175 for 5-day window. Equation (1) indicates a “market model”

regression12 that the treasury rate is the market rate which determines the mortgage rate. The vector

of coefficients 1 measure the daily abnormal returns of the mortgage rate on day t. Equation (2)

10 From the data, the windows for event on 11/25/2008 and the one on 12/1/2008 overlapped. 11 Usually return means the percentage change of a variable, here I name change of rate as return since mortgage and

Treasury rates are already in percent. Also, since “abnormal return” is widely used by researchers doing event

studies, it is used in this paper instead of “abnormal change” to avoid confusions. 12 The name “market model regression” can be found in Degryse, Kim and Ongena (2009).





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is a “constant mean return” regression without tTR . This regression can be regarded as a

robustness check for Equation (1). Equation (3)13 estimates the relation between tTR and tD .

Equation (4) combines tMR and tTR as one dependent variable. Although someone would

argue that Equation (4) is a restricted version of Equation (1), it would not be the case when

GARCH terms are added into the model.

Our analysis 14 shows that the dependent variables in Equation (1) to (4) are serially

correlated and heteroskedastic, the characteristics that are typically modelled using GARCH

models. We first select a GARCH model that best fits each of the return series ( tMR , tTR and

tSpread ), and then add the controls and event dummies to build the full model. To better capture

the patterns of error terms, we suppose them following skewed t distributions (Makenzie et al.,

2004).

After adding ARMA and GARCH items to Equation (1) to (4), the complete model is

specified as

7

1,0 1,1 1, 1 1 1,

1

' ' −

=

= + + + + +t t i t i t t t

i

MR TR MR D X , (5)

1, 1, 1, =t t te , 𝑒1,𝑡 ~ 𝑆𝑘𝑒𝑤𝑒𝑑 𝑡(0,1; 𝜗1, 𝑟1),

2 2 2 2

1, 1 1, 1 1 1, 1 1 1 1, 2(1 ) − − −= + + − −t t t t ;

7

2,0 2, 2 2 2,

1

' ' −

=

= + + + +t i t i t t t

i

MR MR D X , (6)


13 A constant mean return regression is preferred to a market model regression here because there is no single

reference series (market rate) that simulates or determines the Treasury rate. 14 The identification of issues, ARMA and GARCH selections, and the skewed t distribution analysis are discussed

in Appendix, Part 2.





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2 2 2 2

2, 2 2, 1 2 2, 1 2 2 2, 2(1 ) − − −= + + − −t t t t ;

5

3,0 3, 3 3 3,1

' ' −=

= + + + +t t j t tj tj

TR TR D X , (7)


2 2 2

3, 3 3, 1 3 3, 1(1 ) − −= + −t t t ;

6

4,0 4, 4 4 4,

1

' ' −

=

= + + + +t i t i t t t

i

Spread Spread D X , (8)


2 2 2

4, 4 4, 1 4 4, 1(1 ) − −= + −t t t .

In the model, ,j te is the standardized innovation following standard skewed t distribution with

degree of freedom 𝜗𝑗 and skewness 𝑟𝑗 in regression j . The variance 2

.j t of innovation ,j t in

regression j at time t is conditional on past values of the squared innovations (ARCH) and

variances (GARCH). tX is a vector of control variables including unexpected changes of

macroeconomic variables and mortgage rate determinants. As the control variables have minor

impact15 on dependent variables, tX is not included in the regressions here.

For 5-day and 3-day window cases, we add up the abnormal return ( ktAR ) for each day t

from an event window of event k to get the cumulative abnormal return ( kCAR ), which can be

expressed as

2

1

t

k kt

t t

CAR AR=

= , (9)

15 The trivial impacts are mentioned by Altavilla and Giannone (2017), we found similar results in robustness check

part.





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where 1t and 2t represent the lower and upper bounds of days in an event window. For 1-day

window, kCAR is the same as kAR .

5. Results

5.1. Individual events

The regression results are reported in Table 3, 4 and 5 for 1-day, 3-day and 5-day event

window cases. we report abnormal returns (ARs) on event days for 1-day window case and

cumulative abnormal returns (CARs) in event windows for 3-day and 5-day window cases16. Most

of the ARs in 1-day window and CARs in 3-day and 5-day windows for the same event followed

the similar signs and significances with few exceptions. The magnitudes of CARs in 3-day and 5-

day windows were not always greater than those of ARs on event days for the same event, which

suggests the high volatility of ARs within an event window.

Large effects on the mortgage rate were found during the days when new rounds of QE or

QE tapering were hinted, the effects from any further news conveying a continuation of the current

QE policy dwindled. For example, when QE1 was first announced on 11/25/2008 for purchasing

GSE debts and MBSs, ARs and CARs were significantly negative for all three window sizes.

Specifically, AR was -0.121 percent on event day and CAR was -0.149 percent in 3-day window.

For other QE events followed17, AR and CARs were all negative but in smaller magnitudes. On

5/22/2013, when Bernanke remarked the potential tapering of asset purchases during his speech,

AR went up to 0.068 percent on event day, and CARs were 0.114 and 0.120 in 3-day and 5-day

16 The ARs for calculating CARs can be found in Appendix, Table A3 and A4. 17 Events on 1/28/2009 and 3/18/2009. Events on 12/1/2008 and 12/16/2008 are not counted since they both focused

on Treasury bond purchases.





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windows. For other QE tapering events followed18, AR and CARs were all positive but in smaller

magnitudes. Significant ARs and CARs were also found in other announcement dates suggesting

new rounds of QE19.

The macroeconomic news largely deviating from market expectation contaminate the QE

announcement effects on the Treasury rate more than on the mortgage rate. For example, when

Bernanke mentioned additional QE “should further action” on 8/27/2010, there was supposed to

be downward pressure on both Treasury rate and mortgage rate. However, event day AR and 3-

day CAR of mortgage rate were significant and negative at -0.175 and -0.127 percent, while those

of Treasury rate were significant and positive at 0.165 and 0.030 percent. Only when we increase

the window size to 5-day, the CARs of the Treasury rate switch to negative. The positive AR and

CARs of Treasury rate can be attributed to a better-than-expected report on U.S. economic growth

by Department of Commerce20, which had little effects on the mortgage.

Market expectations before QE announcements had strong effect on both mortgage rate

and Treasury rate. For example, the Fed on 9/13/2012 announced an increase in purchases of MBSs,

though public expected the purchase to be mix of MBSs and Treasury bonds21. On the event day,

a significant and negative AR of mortgage rate at -0.046 percent coexisted with an insignificant

and positive AR of Treasury rate at 0.047 percent, while 3-day and 5-day CARs of the mortgage

rate were -0.080 and -0.045 percent compared to those of the Treasury rate at 0.265 and 0.230

percent.

If no other events happened on the same day, an unexpected announcement of increase in

18 Events on 6/19/2013 and 12/18/2013. 19 They are 8/27/2010 for QE2, 8/9/2011 for OT, 8/22/2012 for QE3. 20 Department of Commerce reported real GDP growth of 1.6% in second quarter of 2010, which was higher than

the consensus value of 1.3%. 21 See Bloomberg article 9/13/2012.





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MBS purchases shocked the mortgage rate more than the Treasury rate, while an unexpected

announcement of increase in Treasury purchases shocked the Treasury rate more than the mortgage

rate22. For instance, on 12/1/2008, when Bernanke mentioned possible longer-term Treasury bond

purchases, the Treasury rate declined tremendously but the mortgage rate did not. Specifically, AR

and 3-day CAR of Treasury rate were significant at -0.166 and -0.342 percent, while those of

mortgage rate were both insignificant at 0.011 and 0.005 percent. Different signs of AR or CAR

of the mortgage rate and the Treasury rate lead to large and positive AR and CAR of the spread.

The similar phenomenon were found in 3/18/2009 when the Fed officially announced Treasury

bond purchase in QE1, 8/10/2010 when the Fed announced to reinvest principal payments from

MBSs in Treasury bonds, 9/21/2011 when the Fed announced to purchase long-term and sell same

amount of short-term Treasury bonds, and 9/13/2012 when the Fed announced the additional

purchase of MBSs in QE3.

The signs and magnitudes of ARs and CARs were not consistent for a few events. When

market took days after the QE announcement to absorb the news or the news had been already

priced in the days leading up to the announcement, insignificant AR and significant CARs were

found for that event (e.g., events on 8/12/2009 and 7/15/2014). When the effect of QE

announcement was transitory, significant AR and insignificant CARs were found for that event

(e.g., events on 12/12/2012 and 7/30/2014).

5.2. Long run effects

Since there are right hand side lagged dependent variables in all four regressions (i.e.,

equation (5)- (8)), the announcement effects on dependent variables will last into the future through

22 An exception is the first QE announcement on 11/25/2008 which announced only MBS purchase, but both

mortgage rate and treasury rate had significantly large and negative ARs and CARs.





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them. In long run, if we assume a steady state ∆𝑀𝑅𝑡 = ∆𝑀𝑅𝑡−1 = ⋯ = ∆𝑀𝑅𝑡−7, from equation

(5) or (6), the total AR of the mortgage rate for event 𝑘 can be calculated as

1,

7 7

1, 1,

1 1

1 1

= =

= =

− −

k kk

i i

i i

ARTAR ,

(10)

and total CAR of the mortgage rate for event 𝑘 as

2

1

1,

7 7

1, 1,

1 1

1 1

=

= =

= =

− −

t

t

t t kk

i i

i i

CARTCAR . (11)

TARs and TCARs of treasury rate and spread could also be derived by similar strategy from

equation (7) and (8). Given that both TARs and TCARs are non-linear transformations of

regression parameters, we incorporate Delta Method23 to find the asymptotic standard errors for

them. Table 6 reports the estimates and standard errors of TARs and 3-day and 5-day TCARs from

four regressions.

From Table 6, TAR and TCARs for the same event had less magnitudes than AR and CARs,

while the signs and significances did not vary so much. That being said, although the long run

effects of QE announcement on the mortgage rate shrunk in long run, the directions of long run

effects stayed the same as short run effects. The muted long run effects are due to the negative

autocorrelations and partial autocorrelations within the data series as we found earlier. Moreover,

the long run effects were less volatile than short run effects since the standard errors of TAR and

TCARs for the same event decreased compared to those of AR and CARs.

23 The Delta Method estimates the standard errors of 1st order Taylor expansion of ( )f , which can be expressed as

( ( )) ( ) ( ) ( )TSE f f Cov f . In the equation, ( )f is a transformation of regression parameter vector and

( )f is the gradient of ( )f . Here, ( )f is TAR or TCAR.





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5.3. Comparing the results with literature

Next, we aggregate the ARs in each round of QE to find the cumulative effects of

announcements. Table 7 reports the cumulative effects24 for 1-day, 3-day and 5-day window cases

from my estimation and the ones from other studies. My estimation is consistent with other studies

that cumulative effects on both the mortgage rate and the Treasury rate were greater in QE1 than

in other rounds of QE. However, the magnitudes of the effects in QE1 in my study were only half

of those in other studies25. The evidence suggests that event studies using OLS without controlling

for the serial correlation and the conditional heteroscedasticity within the data series overestimate

the QE announcement effects on interest rates in general. The spread between the mortgage rate

and the Treasury rate increased around days of announcements cumulatively in all rounds of QE

except for QE3, which were expected to boost the risk-taking behaviors of investors as a goal of

the Fed’s QE.

6. Events grouped by announcement type and QE round

6.1. Regressions with grouped event dummies

In order to generalize QE announcement effects on the mortgage rate, we next group all

QE events26 by type of asset purchased (i.e., the MBSs, the Treasury Securities or Both as shown

in Table 2, Column 6), increase or decrease of purchase (i.e., as shown in Table 2, Column 6), and

round of QE (i.e., QE1, QE2, OT, QE3 and tapering as shown in Table 2, Column 2). Dummy

24 The cumulative effects are graphed in Appendix, Figure A4. 25 The cumulative abnormal returns of the mortgage rate and the Treasury rate for 1-day window in my study were -23 and -54 basis points, while those in other studies were all around -100 basis points. 26 A total of 31 events are in the sample at this part. As we discuss in Part 5, event on 8/27/2010 is excluded since it

is contaminated by better Economy report on the same day. Event on 11/3/2010, 6/20/2012 and 12/12/2012 are

excluded given that these 3 events were well expected by the market. In fact, plenty of other researchers exclude

those four events in their studies as well. After the deletion, my sample of events is consistent with Krishnamurthy

and Vissing-Jorgensen (2011), Bowman et al. (2015) and Borrallo et al. (2016).





16

variables are created with value 1 on days of grouped event and 0 on other days. For example, the

dummy variable “QE1_MBS_Increase” has value 1 on the days when Fed announced increase of

MBS purchases during QE1 and 0 on other days. We replace the individual event dummies in

equation (5) to (8) by the new dummies and run four regressions. The regression results for 1-day,

3-day and 5-day windows are reported in Table 8.

Consistent with the result in Part 5, the events of increase in MBS purchases reduced the

mortgage rate more than the Treasury rate, while the events of increase in Treasury purchases

reduced the Treasury rate more than the mortgage rate on event days or in event windows27. From

Table 8, Panel A, on event days of increase in MBS purchases in QE1 and QE3, ARs of the

mortgage rate were large and negative at -0.095 and -0.046 percent, while ARs of the Treasury

rate were small and positive at 0.004 and 0.047 percent. In longer window cases from Table 8,

Panel B and C, CARs of the mortgage rate were significantly negative at -0.166 and -0.079 percent

for 3-day window, and -0.115 and -0.042 percent for 5-day window, while CARs of the Treasury

rate were small or even positive at -0.018 and 0.264 percent for 3-day window, and 0.220 and

0.230 percent for 5-day window. On the other hand, large and negative ARs and CARs of the

Treasury rate were found during events of increase in Treasury purchases, but the mortgage rate

was not significantly affected. For example, the AR on event day and 3-day and 5-day CARs of

the Treasury rate for events of increase in Treasury purchases in QE1 were large and negative at -

0.223, -0.358 and -0.651 percent, while those of the mortgage rate were minute and event positive

at 0.010, 0.004 and -0.049 percent. The similar results were found in other rounds of QE28 as well.

The different responses of the mortgage rate and the Treasury rate for those two types of

events lead to different movements of mortgage-Treasury rate spread. Generally, the spread

27 ARs and CARs of mortgage rate here are from market model regressions. 28 Events of only increasing Treasury purchases happened in QE2 and OT periods too.





17

narrowed during events of increase in MBS purchases, while the spread widened during events of

increase in Treasury purchases. For the 5-day window case, CARs of the spread were -0.318 and

-0.208 percent for events of increase in MBS purchases in QE1 and QE3 respectively. In contrast,

CARs of spread were 0.505, 0.083 and 0.279 percent for events of increase in Treasury purchases

in QE1, QE2 and OT correspondingly. The similar results were found in 1-day or 3-day windows.

In other words, the credit risk of holding MBSs over Treasury securities were reduced when the

Fed announced to increase MBSs purchases, while the risk was intensified when Fed announced

to increase Treasury purchases.

The events of decrease in MBS purchases and decrease in Treasury purchases were not

quite consistent with increased purchases. Although the event of decrease in MBS purchases in

QE1 lead to positive ARs and CARs of both the mortgage rate and the Treasury rate, the AR on

event day and CARs in 3-day and 5-day event windows of the mortgage rate were all less than

those of the Treasury rate. Moreover, CARs of the mortgage rate and the Treasury rate were both

negative in 3-day and 5-day windows for event of decrease in Treasury purchases in QE1.

QE Tapering events enhanced both the mortgage rate and the Treasury rate, however, the

effects were limited. The AR and 3-day and 5-day CARs of the mortgage rate for tapering events

were only 0.006, 0.021 and 0.032 percent, while those of the Treasury rate stayed as low as 0.008,

0.009 and 0.000 percent. As we showed in Part 5, although significant and positive AR on the

event day and CARs in 3-day and 5-day windows of the mortgage rate and the Treasury rate existed

during first few events in tapering period, the insignificant and smaller AR and CARs during latter

events diluted the average effects of tapering events.

6.2. Evolution of CARs for grouped QE events





18

To see how the mortgage rate, the Treasury rate and the spread moved on each day in a 5-

day event window of grouped events, the evolutions of CARs in a 5-day event window are shown

in Figure 2.

For QE events targeting at both MBS and Treasury purchases (i.e., in QE1, OT and QE3),

the mortgage rate and the Treasury rate decreased either on event days or one day after event days.

The decrease of the Treasury rate was larger in magnitude than the decrease of the mortgage rate

which is in accordance to Wright’s (2012) finding of smaller effects on private sector rates than

on Treasury yields of QE shocks. Thus, on average the mortgage-Treasury rate spread expanded

on event days and then narrowed the days after.

From Figure 2, Panel A, during events of increase in MBS purchases (i.e., in QE1 and

QE3), the mortgage rate declined considerably on event day and one day after the event day.

Specifically, CARs of the mortgage rate one day after the event day slumped to -0.14 and -0.06

percent for events in QE1 and QE3 respectively. However, from Figure 2, Panel C, during the

same event, the Treasury rate increased on event day and one day after the event. Specifically,

CARs of the Treasury rate one day after the event day surged to 0.19 and 0.29 percent for events

in QE1 and QE3.

In contrast, from Figure 2, Panel A, during events of in increase Treasury purchases (i.e.,

in QE1, QE2 and OT), the mortgage rate barely declined or even rose up a bit on event day and

days after the event day. CARs of the mortgage rate one day after the event day remained 0.00, -

0.02 and -0.01 percent for events in QE1, QE2 and OT respectively. Again, from Figure 2, Panel

C, during the same event, the Treasury rate dropped sharply on the event day and after. CARs of

the Treasury rate one day after the event day collapsed to -0.61, -0.13 and -0.50 percent for events

in QE1, QE2 and OT. The evidence strongly supports the previous conclusion that events of





19

increase in MBS purchases lowered the mortgage rate more than the Treasury rate, while events

of increase in Treasury purchases lowered the Treasury rate more than the mortgage rate around

event days.

From Figure 2, Panel D, the mortgage-treasury rate spread went down largely after

increases in MBS purchases and went up after increases in Treasury purchases. The graphs show

significant and negative CARs at -0.31 and -0.29 percent one day after the event day for events of

MBS purchases in QE1 and QE3, while significant and positive CARs at 0.55, 0.06 and 0.38

percent one day after the event day for events of Treasury purchases in QE1, QE2 and OT. The

results are in line with the previous conclusion from regression analysis that announcements of

increase in MBS purchases lessened the credit risk of holding mortgages over Treasury securities,

while announcements of increase in Treasury security purchases augmented the risk.

Part 6.1 shows that the tapering announcements had limited effects on the mortgage rate

and the Treasury rate. This result also applies to here that the CARs of both rates were trending up

in small magnitudes. In addition, Figure 2 shows that the upward movement of the mortgage rate

after tapering announcements was more persistent than that of the Treasury rate, which boosted

the spread and increased the risk of holding mortgages over Treasury securities.

7. Robustness Checks

7.1 Adding more controls into the model

We check if our model is better fitted by adding more control variables. First,

macroeconomic surprises are thought to have impact on interest rate. Patrabansh et al. (2014) and

Thornton (2017) mention the abnormal changes of Treasury rate were attributed to both QE

announcements and macroeconomic news. However, Altavilla and Giannone (2017) show that the





20

effects of macroeconomic shock were “marginal” on average and the estimation results did not

change so much with the inclusion of surprise components29. We pick unexpected changes of

log(CPI) and unexpected changes of the unemployment rate (UER) to identify macroeconomic

surprises30.

Second, shocks to the determinants of mortgage rates and MBS yields might affect the

mortgage rate. Hancock and Passmore (2011, 2012, 2015) propose some determinants of mortgage

rates and MBS yields, from which we select the control variables by using two criteria. One is that

the variables selected should not be significantly affected by QE announcements. The other one is

that the variables should contain news about mortgage rates. Only two variables from their study

are in line with these two standards, which are Case-Shiller Home Price Index (HPI) and

unemployment rate (UER). From Hancock and Passmore (2011, 2012), HPI measures the costs of

origination and servicing. Along with UER, they both reflect the credit risk of mortgage.

Although the values of three control variables (i.e., ln( )CPI , ln( )HPI and tUER ) are

monthly reported, the data are identified with respect to the dates they are announced. Since all

three variables do not have unit root31, we use ARMA models to estimate the expected values of

them. Then the unexpected part of these three variables equals to the value of original data minus

the expected value. Based on AIC, the best models fitting ln( )CPI , ln( )HPI and tUER are

ARMA(3,2), ARMA(2,0) and ARMA(1,5) respectively32.

Next, we run the four regressions as equation (5) to (8) by adding ln( )tCPI , ln( )tHPI ,

29 They claim that since only important events are considered, the effect of which were tremendous and take over

macroeconomic news within the event window. 30 Unexpected change of GDP growth is another indicator of macroeconomic surprises. However, it is hard to

estimate since quarterly GDP growth rate would be revised several times in a long time span by Bureau of Economic

Analysis (or U.S. Department of Commerce). 31 Dickey-Fuller unit root test results are found in Appendix, Table A1. 32 All three ARMA models are sufficient. From Appendix, Figure A5 and Figure A6, we can see that the ACF and

PACF of residuals in three models for all lags are insignificant.





21

tUER , 1ln( )tCPI − , 1ln( )tHPI − , 1tUER − , 2ln( )tCPI − , 2ln( )tHPI − , 2tUER − to the right hand

side of equations with 1-day, 3-day and 5-day windows. From the results, we find that values and

significances of ARs and CARs with controls are similar to what we found without controls for all

three window sizes. For that reason, we only report the coefficient estimates of these newly added

control variables in the regressions for 5-day window33 in Table 9. From the table, most of the

coefficient estimates are statistically insignificant and have small magnitudes and standard errors.

Specifically, one unit change of control variables only accounts for less than 0.05 percent change

of each dependent variable. In conclusion, the effects of control variables identifying

macroeconomic shock and determining mortgage rate are trivial, thus we are not worried about

not including them in the model.

7.2 Using 10-year instead of 30-year Treasury rate

Since most of the 30-year mortgages are paid off or refinanced within 10 years, the 10-year

Treasury rate is widely regarded as the risk- free rate determining the 30- year mortgage rate rather

than the 30-year Treasury rate. In the period of our interest from 1/1/2008 to 12/31/2015, the

correlation between the 30-year mortgage rate and the 10-year Treasury rate is 0.912, which is

greater than the correlation between the 30-year mortgage rate and the 30-year Treasury at 0.807.

To compare the results, we replace the 30-year Treasury rate with the 10-year Treasury

rate in equation (5) and run the regression to find ARs and CARs of the 30-year mortgage rate for

grouped events. CARs of mortgage rates from regressions controlling for the 10-year and 30-year

Treasury rate separately are reported in Table 10.

33 Similar studies using 1-day and 3-day window sizes are done and result in the similar outcomes as the ones

without including these 3 determinants in the models, and we don’t report the results in this paper.





22

There is no major difference between the results of regressions controlling for the 10-year

Treasury rate and the 30-year Treasury rate. Both the value and standard error of CAR for same

grouped events were similar in magnitude except for events of increase in Treasury purchases in

QE1 and increase in both purchases in OT. In fact, CARs for these two grouped events were

insignificant and had small values in terms of both regressions with different controls.

Some authors (Sirmans et al., 2015) propose that the 10-year LIBOR swap rate is superior

to the 10-year Treasury rate as determination. We replace the 30-year Treasury rate by the 10-year

swap rate in my model and find that the results do not vary so much both statistically and

economically. In conclusion, it is not much different between choosing the 10-year and 30-year

Treasury rate as the market rate in my model.

8. Conclusions

This paper uses event study methodology to estimate the effects of the Fed’s QE

announcements on the 30-year mortgage rate. In the analysis, we apply autoregressive model with

IGARCH errors following skewed t distribution to run the regressions with three different window

sizes.

We find that although the QE announcements suggesting the start of a new QE round or

tapering affected the mortgage rate enormously, the effects from further news conveying a

continuation of the current QE policy diminished. Macroeconomic news largely different from the

market expectation on the same day of a QE event obscured the QE announcement effect on the

Treasury rate, but did not shadow the QE announcement effect on the mortgage rate so much. If

the market expectation was in the same direction as a QE announcement, the effect of this

announcement on the mortgage rate would be minimized and vice versa. Signs and magnitudes of





23

AR and CARs for the same event might not be confirmative if the announcement effect was

transitory or the news had already been priced into mortgage rate before the announcement day.

The calculation of TCARs tells us that although long run effects of QE announcements were less

than short run effects, they did not fade away too much. We also find that event studies without

controlling for serial correlation and conditional heteroscedasticity within data series

overemphasize the QE effects on interest rates in general.

After grouping QE events by the announcement type and the QE round, we conclude that

the mortgage rate decreased more than the Treasury rate and the spread narrowed during the events

of increase in MBS purchases. Meanwhile, the Treasury rate decreased more than the mortgage

rate and the spread expanded during the events of increase in Treasury security purchases. Finally,

although QE tapering events had limited effects on both the mortgage rate and the Treasury rate

on average, they boosted the credit risk of holding mortgage assets instead of Treasury bonds.





24

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Rates? Journal of Monetary Economics 58 (2011) 498-514.

Hancock, D. and Passmore, S., 2012. The Federal Reserve’s Portfolio and Its Effects on Mortgage Markets

(March 6, 2012). FEDS Working Paper No. 2012-22.

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26

Tables and Graphs

Figure 1: Mortgage Rates, Treasury Rates and MBS Yields

This figure shows time series of the 30-year Mortgage Rate, the 30-year Treasury Rate and the 30-year Freddie Mac MBS current coupon yield from

January 3, 2000 to December 31, 2015. The different rounds of QEs are marked by different colors.

Source: Bloomberg and U.S. Treasury.





27

Table 1: Summary of Data

This table reports the descriptive statistics of the variables used in this paper. All the rates are in percentage and the indices are in level. The sample

period spans from 1/1/2008 to 12/31/2015.

Obs. Frequency Mean Median SD Min Max Skewness Kurtosis

30-year Mortgage Rate 1998 Daily 4.52 4.34 0.75 3.36 6.51 0.65 -0.38

30-year Treasury Rate 1998 Daily 3.63 3.61 0.67 2.25 4.85 0.08 -1.34

10-year Treasury Rate 1998 Daily 2.72 2.63 0.71 1.43 4.27 0.24 -1.14

Freddie Mac 30-year MBS Current Coupon 1998 Daily 3.674 3.438 0.959 1.522 6.177 0.680 -0.121

Consumer Price Index 96 Monthly 225.859 227.196 8.892 211.398 238.302 -0.131 -1.497

Case-Shiller House Price Index 96 Monthly 155.62 148.02 14.85 137.08 184.03 0.45 -1.34

Unemployment Rate 96 Monthly 7.57 7.75 1.63 4.90 10.00 -0.15 -1.35

Note: Data of the 30-year mortgage rates and the Freddie Mac 30-year MBS current coupon yield come from Bloomberg; Data of the 30-year

Treasury rate and the 10-year Treasury rate come from U.S. Department of Treasury; Data of the consumer price index, the Case-Shiller house price

index and the unemployment rate come from Federal Reserve Bank of St. Louis.





28

Table 2: The Federal Reserve Bank’s Quantitative Easing (QE) Announcements

This table reports the QE announcement events ordered by event date. There are 35 events in total, and each of

them is assigned to a certain QE program, event type and event group. “Increase”, “Decrease” and “Com” tell

us if the event is about increasing asset purchases, decrease asset purchases or only a communication to the

market.

Date Program Event Announcement Group

11/25/2008 QE1 FOMC

Meeting

The FOMC “will purchase $100 billion in

GSE debt and $500 billion in GSE MBS.” MBS_Increase

12/1/2008 QE1 Bernanke

Speech

He stated Fed “could purchase longer-term

Treasury or agency securities.” T_Increase

12/16/2008 QE1 FOMC

Meeting

The Fed cut federal funds rate target from 1%

to 0-0.25%, was “ready to expand its

purchases of agency debt and MBSs as

conditions warrant”, and suggested of

extending QE to Treasury purchases.

Both_Increase

1/28/2009 QE1 FOMC

Meeting

The Fed was ready to expand the quantity and

duration of MBS purchases. MBS_Increase,

3/18/2009 QE1 FOMC

Meeting

The FOMC “will purchase up to an additional

$750 billion of agency MBSs with a total of

$1.25 trillion, up to $100 billion agency debt

this year with a total of $200 billion and up to

$300 billion longer-term Treasury securities

over the next six months.”

Both_Increase

8/12/2009 QE1 FOMC

Meeting

The FOMC “decided to gradually slow the

pace” of Treasury purchases. T_Decrease

9/23/2009 QE1 FOMC

Meeting

The FOMC “will gradually slow the pace” of

MBS purchases. MBS_Decrease

11/4/2009 QE1 FOMC

Meeting

The FOMC “will purchase a total of $175

billion of agency debt” instead of $200 billion.

It also “will gradually slow the pace of its

purchases of agency debt and MBSs and these

transactions will executed by the end of first

quarter of 2010.”

MBS_Decrease

8/10/2010 QE2 FOMC

Meeting

The FOMC “will keep constant the Fed’s

holdings of securities … by reinvesting

principal payments from agency debt, agency

MBSs in longer-term Treasury securities.” It

also “will continue roll over Treasury

securities holdings.”

T_Increase


Speech

He suggested additional QE “should further

action prove necessary.” T_Increase

9/21/2010 QE2 FOMC

Meeting

The FOMC “is prepared to provide additional

accommodation if needed.” T_Increase


Speech

Bernanke commented the drawbacks of large

scale asset purchases. T_Decrease

11/3/2010 QE2 FOMC

Meeting

The FOMC “intends to purchase a further

$600 billion of longer term Treasury securities

by the end of second quarter of 2011, at a pace

of about $75 billion per month.”

T_Increase





29

8/9/2011 OT FOMC

Meeting

The FOMC “is prepared adjust those

(securities) holdings as appropriate.” Both_Increase

8/26/2011 OT Bernanke

Speech

He announced “employ its tools … to promote

a stronger economic recovery.” Both_Increase

9/21/2011 OT FOMC

Meeting

The FOMC “intends to purchase, by the end of

June 2012, $400 billion of Treasury securities

with remaining maturities of 6 years to 30

years and sell an equal amount of Treasury

securities with remaining maturities of 3 years

or less”.

T_Increase

6/20/2012 OT FOMC

Meeting

The FOMC decided to “continue through the

end of the year its program to extend average

maturity of its holdings of securities.”

T_Increase

8/22/2012 OT/QE3

FOMC

Minutes

Released

FOMC members “judged that additional

monetary accommodation would likely be

warranted fairly soon.”

Both_Increase

8/31/2012 OT/QE3 Bernanke

Speech

He remarked about unconventional monetary

policy tools. Both_Increase

9/13/2012 OT/QE3 FOMC

Meeting

The FOMC “will purchase additional agency

MBSs at a pace of $40 billion per month”,

along with OT will together increase “holdings

of longer-term securities by about $85 billion

each month through the end of the year.”

MBS_Increase

12/12/2012 OT/QE3 FOMC

Meeting

The FOMC “will purchase longer—term

Treasury securities after OT is completed at

the end of the year, initially at a pace of $45

billion per month.”

T_Increase

5/1/2013 Tapering FOMC

Meeting

The FOMC “is prepared to increase or reduce

the pace of its purchase.” Tapering

5/22/2013 Tapering

Bernanke

Speech and

testimony

He remarked about a potential “step down” in

the pace of asset purchases. Tapering

6/19/2013 Tapering

Bernanke’s

Press

Conference

He said “If the incoming data are broadly

consistent with this forecast, … be appropriate

to moderate the monthly pace of purchases

later this year.”

Tapering


Meeting

The FOMC “Beginning in January … will add

to its holdings of agency MBSs … $35 billion

rather than $40 billion per month, long-term

Treasury securities … $40 billion rather than

$45 billion per month.”

Tapering


Meeting

The FOMC “Beginning in February … will

add to its holdings of agency MBSs … $30

billion rather than $35 billion per month, long-

term Treasury securities … $35 billion rather

than $40 billion per month.”

Tapering


Meeting

The FOMC “Beginning in April … will add to

its holdings of agency MBSs … $25 billion


Tapering





30




Meeting

The FOMC “Beginning in May … will add to





Tapering

5/7/2014 Tapering Yellen

Testimony

She said “further measured reductions in asset

purchases were appropriate.” Tapering


Meeting

The FOMC “Beginning in July … will add to





Tapering


Testimony

She said “will make further measured

reductions in the pace of asset purchases at

upcoming meetings.”

Tapering


Meeting

The FOMC “Beginning in August … will add





Tapering


Speech

She said “we have reduced our pace of asset

purchases and expect to complete this program

in October.”

Tapering


Meeting

The FOMC “Beginning in October … will add





Tapering


Meeting

The FOMC “decided to conclude its asset

purchase program this month.” It is also

“maintaining its existing policy of reinvesting

principal payments from its holdings of agency

debt and agency MBSs in agency MBSs and of

rolling over maturing Treasury securities at

auction.”

Tapering

Note: The event on 8/27/2010 coincided with the better economy report released by Department of Commerce

on the same day. Events on 11/3/2010, 6/20/2012 and 12/12/2012 were well anticipated by the market.





31

Table 3: Abnormal Returns (ARs) in 1-day Windows

We run four regressions (equation (5) to (8)) with 1-day event window. For each regression, the ARs and

standard errors (SEs) on QE event days are reported in Panel A. Coefficient estimates and SEs of control

variables and GARCH components are reported in Panel B.

Panel A:

(5) (6) (7) (8)

Mortgage Rate Mortgage Rate Treasury Rate Spread

AR SE AR SE AR SE AR SE

Mean Model

11/25/2008 -0.121** 0.048 -0.165*** 0.052 -0.166* 0.095 0.002 0.079

12/1/2008 0.011 0.046 -0.053 0.044 -0.220** 0.093 0.205*** 0.076

12/16/2008 0.039 0.043 0.008 0.040 -0.127 0.091 0.129* 0.077

1/28/2009 -0.066 0.043 -0.012 0.043 0.214*** 0.078 -0.225*** 0.078

3/18/2009 0.064** 0.028 -0.005 0.026 -0.239*** 0.072 0.202*** 0.076

8/12/2009 -0.027 0.031 -0.001 0.038 0.085 0.073 -0.068 0.054

9/23/2009 0.051* 0.030 0.055 0.034 0.006 0.064 0.054 0.052

11/4/2009 0.026 0.035 0.047 0.037 0.083 0.054 -0.050 0.055

8/10/2010 0.043** 0.018 0.044* 0.022 -0.013 0.062 0.052 0.047

8/27/2010 -0.175*** 0.017 -0.134*** 0.019 0.165*** 0.053 -0.266*** 0.050

9/21/2010 0.000 0.059 -0.002 0.057 -0.079 0.060 0.036 0.069

10/15/2010 0.117** 0.055 0.156*** 0.051 0.088* 0.051 0.028 0.069

11/3/2010 -0.055 0.044 -0.014 0.047 0.171*** 0.050 -0.152** 0.060

8/9/2011 0.040*** 0.015 0.002 0.019 -0.122* 0.072 0.151** 0.060

8/26/2011 -0.017 0.023 -0.032 0.029 -0.054 0.090 0.039 0.062

9/21/2011 0.020 0.018 -0.024 0.020 -0.161** 0.075 0.161*** 0.057

6/20/2012 0.016 0.016 0.014 0.017 -0.004 0.051 0.016 0.037

8/22/2012 -0.048** 0.020 -0.061*** 0.023 -0.071 0.046 0.001 0.031

8/31/2012 0.022 0.017 0.007 0.019 -0.061 0.039 0.061* 0.033

9/13/2012 -0.046*** 0.016 -0.034* 0.020 0.047 0.040 -0.104*** 0.034

12/12/2012 -0.026*** 0.010 -0.003 0.013 0.080** 0.032 -0.084*** 0.023

5/1/2013 -0.010 0.016 -0.024 0.019 -0.040 0.035 0.020 0.026

5/22/2013 0.068*** 0.017 0.091*** 0.024 0.074* 0.040 0.025 0.026

6/19/2013 0.036 0.037 0.055 0.044 0.084** 0.041 -0.002 0.045

12/18/2013 0.012 0.023 0.019 0.027 0.030 0.034 -0.001 0.028

1/29/2014 -0.003 0.020 -0.022 0.025 -0.055* 0.032 0.029 0.027

3/19/2014 0.045*** 0.017 0.053** 0.023 0.037 0.036 0.030 0.031

4/30/2014 -0.024 0.037 -0.037 0.042 -0.020 0.035 -0.003 0.048

5/7/2014 -0.026 0.046 -0.002 0.044 0.020 0.035 -0.063 0.053

6/18/2014 0.009 0.031 0.005 0.033 -0.009 0.035 0.010 0.040

7/15/2014 0.016 0.027 0.023 0.024 0.006 0.035 0.005 0.037

7/30/2014 -0.106** 0.042 -0.091** 0.044 0.093** 0.035 -0.148** 0.059

8/22/2014 0.009 0.050 -0.007 0.058 -0.028 0.036 0.046 0.058

9/17/2014 0.008 0.041 0.015 0.046 0.017 0.039 -0.013 0.049

10/29/2014 0.013 0.038 0.020 0.034 0.009 0.032 0.004 0.049





32

Panel B:

(5) (6) (7) (8)

Estimate SE Estimate SE Estimate SE Estimate SE

Mean Model

Intercept -0.001*** 0.001 -0.002*** 0.001 -0.001 0.001 0.000 0.001

t

TR 0.280*** 0.013

1− tMR -0.094*** 0.023 -0.004 0.023

2− tMR -0.032 0.020 -0.033 0.021

3− tMR -0.031 0.021 -0.052** 0.021

4− tMR -0.018 0.021 -0.044** 0.020

5− tMR -0.041* 0.021 -0.024 0.022

6− tMR -0.009 0.021 0.014 0.021

7− tMR -0.037* 0.020 -0.048** 0.021

1−

tTR -0.044* 0.023

2−

tTR -0.041* 0.023

3−

tTR -0.028 0.023

4−

tTR -0.050** 0.023

5−

tTR -0.048** 0.023

1− tSpread -0.234*** 0.023

2− tSpread -0.056** 0.023

3− tSpread -0.033 0.023

4− tSpread -0.060** 0.023

5− tSpread -0.082*** 0.023

6− tSpread -0.062*** 0.022

Variance

Model

2

1 −t 0.084*** 0.016 0.103*** 0.015 0.039*** 0.006 0.047*** 0.000

2

1 −t 0.403* 0.211 0.249* 0.128 0.961*** NA 0.953*** NA

2

2 −t 0.513*** 0.649***

Quality of

Model

Log Likelihood 3720.612 3484.149 2939.588 2950.285

AIC -3.678 -3.442 -2.900 -2.910

BIC -3.544 -3.311 -2.777 -2.784

Note: ***, ** and * indicate statistical significance at 0.01, 0.05, 0.1 levels.





33

Table 4: Cumulative Abnormal Returns (CARs) in 3-day Windows

We run 4 regressions (equation (5) to (8)) with 3-day event window (i.e., t= -1, 0, 1). For each regression, the

CARs and standard errors (SEs) on QE event days are reported in Panel A. Coefficient estimates and SEs of

control variables and GARCH components are reported in Panel B.

Panel A:

(5) (6) (7) (8)


CAR SE CAR SE CAR SE CAR SE

Mean Model

11/25/2008 -0.149** 0.065 -0.202*** 0.077 -0.199 0.140 0.006 0.108

12/1/2008 0.005 0.063 -0.098 0.076 -0.342** 0.132 0.274** 0.106

12/16/2008 -0.146*** 0.053 -0.270*** 0.063 -0.387*** 0.121 0.150 0.097

1/28/2009 -0.084 0.056 -0.020 0.062 0.265** 0.116 -0.284*** 0.097

3/18/2009 -0.006 0.036 -0.037 0.036 -0.094 0.118 0.040 0.095

8/12/2009 -0.065 0.043 -0.079 0.054 -0.070 0.115 -0.003 0.076

9/23/2009 0.036 0.047 0.011 0.057 -0.060 0.101 0.090 0.073

11/4/2009 0.029 0.057 0.076 0.066 0.164* 0.090 -0.105 0.079

8/10/2010 -0.017 0.020 -0.035 0.022 -0.069 0.093 0.057 0.060

8/27/2010 -0.127*** 0.023 -0.121*** 0.030 0.030 0.080 -0.134** 0.064

9/21/2010 -0.140** 0.063 -0.177** 0.078 -0.138 0.091 -0.015 0.090

10/15/2010 -0.007 0.075 0.037 0.085 0.128 0.079 -0.109 0.090

11/3/2010 -0.014 0.065 0.010 0.078 0.070 0.074 -0.088 0.082

8/9/2011 -0.057*** 0.017 -0.135*** 0.029 -0.282** 0.112 0.176*** 0.064

8/26/2011 0.017 0.028 0.020 0.044 0.015 0.136 0.031 0.080

9/21/2011 0.002 0.021 -0.111*** 0.031 -0.392*** 0.113 0.300*** 0.081

6/20/2012 -0.001 0.022 0.003 0.028 0.022 0.078 -0.010 0.053

8/22/2012 -0.118*** 0.023 -0.151*** 0.032 -0.112 0.071 -0.029 0.043

8/31/2012 0.003 0.023 -0.014 0.029 -0.053 0.061 0.037 0.044

9/13/2012 -0.080*** 0.020 -0.007 0.030 0.265*** 0.069 -0.272*** 0.041

12/12/2012 -0.008 0.014 0.027 0.020 0.124** 0.050 -0.089*** 0.033

5/1/2013 0.016 0.024 0.004 0.030 -0.034 0.055 0.029 0.039

5/22/2013 0.114*** 0.025 0.131*** 0.037 0.046 0.062 0.088*** 0.034

6/19/2013 0.246*** 0.051 0.290*** 0.065 0.165*** 0.063 0.129** 0.056

12/18/2013 0.053 0.037 0.065 0.046 0.034 0.055 0.037 0.040

1/29/2014 -0.016 0.031 -0.025 0.038 -0.016 0.054 -0.012 0.036

3/19/2014 0.118*** 0.028 0.127*** 0.038 0.030 0.057 0.097** 0.040

4/30/2014 0.020 0.054 0.000 0.066 -0.049 0.047 0.066 0.067

5/7/2014 -0.127** 0.056 -0.092 0.063 0.046 0.051 -0.170* 0.067

6/18/2014 0.019 0.039 0.037 0.046 0.065 0.053 -0.024 0.051

7/15/2014 0.002 0.038 0.002 0.043 0.004 0.053 0.011 0.050

7/30/2014 0.008 0.061 0.032 0.070 0.068 0.053 -0.042 0.079

8/22/2014 0.042 0.074 0.014 0.089 -0.075 0.052 0.069 0.080

9/17/2014 0.005 0.057 0.008 0.074 0.037 0.060 -0.035 0.072

10/29/2014 -0.009 0.053 0.002 0.059 0.026 0.052 -0.029 0.070





34

Panel B:

(5) (6) (7) (8)


Mean Model

Intercept -0.001*** 0.001 -0.002*** 0.001 -0.001 0.001 0.000 0.001

t

TR 0.280*** 0.013

1− tMR -0.098*** 0.023 0.000 0.023

2− tMR -0.031 0.020 -0.034 0.021

3− tMR -0.026 0.021 -0.054** 0.022

4− tMR -0.007 0.021 -0.039* 0.021

5− tMR -0.052** 0.021 -0.029 0.022

6− tMR -0.011 0.021 0.013 0.021

7− tMR -0.044** 0.020 -0.055** 0.021

1−

tTR -0.040* 0.023

2−

tTR -0.045* 0.024

3−

tTR -0.030 0.024

4−

tTR -0.054** 0.024

5−

tTR -0.047* 0.024

1− tSpread -0.233*** 0.023

2− tSpread -0.042* 0.023

3− tSpread -0.032 0.024

4− tSpread -0.069*** 0.023

5− tSpread -0.090*** 0.023

6− tSpread -0.074*** 0.023

Variance Model 2

1t − 0.087*** 0.016 0.105*** 0.016 0.036*** 0.006 0.039*** 0.008

2

1t − 0.268 0.170 0.176* 0.095 0.964*** 0.961***

2

2t − 0.645*** 0.718***

Quality of Model

Log Likelihood 3795.506 3550.227 2982.218 3005.344

AIC -3.683 -3.438 -2.873 -2.895

BIC -3.352 -3.110 -2.553 -2.572






35

Table 5: Cumulative Abnormal Returns (CARs) in 5-day Windows

We run 4 regressions (equation (5) to (8)) with 5-day event window (i.e., t= -2, -1, 0, 1, 2). For each regression,

the CARs and standard errors (SEs) on QE event days are reported in Panel A. Coefficient estimates and SEs of

control variables and GARCH components are reported in Panel B. The CARs for 11/25/2008 and 12/1/2008

are not reported since they are overlapped.

Panel A:

(5) (6) (7) (8)



Mean Model

11/25/2008 N/A N/A N/A N/A N/A NA N/A N/A

12/1/2008 N/A N/A N/A N/A N/A N/A N/A N/A

12/16/2008 -0.090 0.067 -0.232*** 0.073 -0.484*** 0.137 0.253** 0.115

1/28/2009 -0.082 0.067 0.020 0.079 0.367** 0.145 -0.358*** 0.109

3/18/2009 -0.005 0.046 0.002 0.047 0.043 0.146 -0.051 0.107

8/12/2009 -0.095** 0.047 -0.142** 0.062 -0.152 0.139 0.029 0.091

9/23/2009 0.087* 0.050 0.050 0.059 -0.115 0.121 0.165* 0.086

11/4/2009 -0.053 0.064 -0.003 0.071 0.185* 0.109 -0.179** 0.090

8/10/2010 0.002 0.025 -0.028 0.026 -0.101 0.110 0.109 0.072

8/27/2010 -0.133*** 0.029 -0.142*** 0.033 -0.018 0.095 -0.093 0.076

9/21/2010 -0.104 0.079 -0.149* 0.090 -0.147 0.106 0.010 0.108

10/15/2010 -0.073 0.086 -0.026 0.097 0.154* 0.090 -0.204** 0.104

11/3/2010 -0.029 0.077 0.021 0.089 0.176** 0.086 -0.154 0.097

8/9/2011 -0.108*** 0.019 -0.086** 0.035 0.067 0.143 -0.073 0.079

8/26/2011 0.023 0.032 0.042 0.044 0.081 0.131 -0.005 0.085

9/21/2011 -0.013 0.023 -0.130*** 0.032 -0.381*** 0.118 0.279*** 0.084

6/20/2012 -0.008 0.026 0.010 0.033 0.066 0.097 -0.033 0.061

8/22/2012 -0.067*** 0.024 -0.088** 0.036 -0.102 0.085 0.015 0.050

8/31/2012 -0.010 0.021 -0.016 0.030 -0.015 0.072 -0.002 0.051

9/13/2012 -0.045** 0.022 0.021 0.032 0.230*** 0.081 -0.208*** 0.046

12/12/2012 0.009 0.018 0.035 0.025 0.100* 0.060 -0.063 0.039

5/1/2013 0.039 0.030 0.074** 0.037 0.120* 0.063 -0.048 0.041

5/22/2013 0.120*** 0.030 0.133*** 0.043 0.052 0.067 0.086** 0.037

6/19/2013 0.361*** 0.064 0.448*** 0.078 0.296*** 0.075 0.152** 0.066

12/18/2013 0.041 0.043 0.038 0.051 -0.021 0.064 0.056 0.047

1/29/2014 -0.047 0.034 -0.049 0.041 -0.022 0.063 -0.028 0.041

3/19/2014 0.121*** 0.031 0.127*** 0.041 0.015 0.064 0.110** 0.047

4/30/2014 0.204*** 0.066 0.190*** 0.072 -0.066 0.053 0.232*** 0.076

5/7/2014 -0.181*** 0.058 -0.149** 0.064 0.110** 0.055 -0.262*** 0.070

6/18/2014 0.017 0.044 0.028 0.050 0.035 0.058 -0.003 0.055

7/15/2014 0.296*** 0.040 0.270*** 0.045 -0.096* 0.052 0.352*** 0.054

7/30/2014 -0.010 0.049 0.006 0.054 0.062 0.054 -0.047 0.066

8/22/2014 0.011 0.075 0.015 0.092 -0.043 0.058 0.003 0.080

9/17/2014 0.071 0.069 0.065 0.080 -0.022 0.067 0.062 0.075

10/29/2014 0.023 0.063 0.041 0.072 0.057 0.061 -0.020 0.078





36

Panel B:

(5) (6) (7) (8)


Mean Model

Intercept -0.002*** 0.001 -0.002*** 0.001 -0.001 0.001 0.000 0.001

t

TR 0.280*** 0.013

1− tMR -0.085*** 0.022 0.007 0.022

2− tMR -0.035* 0.020 -0.043** 0.021

3− tMR -0.023 0.021 -0.052** 0.022

4− tMR -0.005 0.020 -0.030 0.021

5− tMR -0.073*** 0.022 -0.046** 0.022

6− tMR -0.004 0.022 0.020 0.022

7− tMR -0.048** 0.021 -0.065*** 0.021

1−

tTR -0.029 0.023

2−

tTR -0.046* 0.024

3−

tTR -0.031 0.024

4−

tTR -0.054** 0.025

5−

tTR -0.038 0.025

1− tSpread -0.220*** 0.023

2− tSpread -0.043* 0.023

3− tSpread -0.027 0.023

4− tSpread -0.060** 0.024

5− tSpread -0.099*** 0.024

6− tSpread -0.075*** 0.024

Variance

Model

2

1t − 0.080*** 0.017 0.099*** 0.018 0.036*** 0.007 0.039*** 0.009

2

1t − 0.309* 0.161 0.203* 0.106 0.964*** 0.961***

2

2t − 0.611*** 0.699***

Quality of

Model

Log Likelihood 3853.002 3608.316 3031.920 3062.150

AIC -3.673 -3.429 -2.854 -2.884

BIC -3.151 -2.910 -2.344 -2.370






37

Table 6: Long Run Effects of QE Announcement

The estimates and standard errors (SEs) of total abnormal returns (TARs) for 1-day window and total cumulative abnormal returns (TCARs) for 3-

day window and 5-day window are reported in the table. SEs are asymptotic standard errors calculated by Delta Method. The TCARs for two events

with overlapped event windows on 11/25/2008 and 12/1/2008 are invalid and denoted as N/A.


(5) (6) (7) (8)


1-day 3-day 5-day 1-day 3-day 5-day 1-day 3-day 5-day 1-day 3-day 5-day

Date TAR SE TCAR SE TCAR SE TAR SE TCAR SE TCAR SE TAR SE TCAR SE TCAR SE TAR SE TCAR SE TCAR SE

11/25/2008 -0.096** 0.038 -0.118* 0.062 N/A N/A -0.139** 0.044 -0.169** 0.073 N/A N/A -0.137* 0.078 -0.163 0.132 N/A N/A 0.001 0.051 0.004 0.097 N/A N/A

12/1/2008 0.008 0.036 0.004 0.058 N/A N/A -0.044 0.037 -0.082 0.073 N/A N/A -0.181** 0.077 -0.281** 0.125 N/A N/A 0.134*** 0.050 0.178* 0.094 N/A N/A

12/16/2008 0.031 0.034 -0.115* 0.050 -0.071 0.065 0.007 0.034 -0.225*** 0.061 -0.192*** 0.071 -0.105 0.075 -0.318*** 0.115 -0.404*** 0.133 0.084* 0.050 0.097 0.088 0.166 0.109 1/28/2009 -0.052 0.034 -0.066 0.053 -0.064 0.065 -0.010 0.036 -0.017 0.059 0.016 0.076 0.177*** 0.064 0.218** 0.109 0.306** 0.142 -0.147*** 0.051 -0.184** 0.087 -0.235** 0.103

3/18/2009 0.050** 0.023 -0.005 0.033 -0.004 0.044 -0.004 0.022 -0.031 0.034 0.002 0.045 -0.197*** 0.061 -0.077 0.113 0.035 0.141 0.132*** 0.050 0.026 0.085 -0.033 0.100

8/12/2009 -0.022 0.025 -0.051 0.040 -0.074 0.046 -0.001 0.032 -0.066 0.052 -0.117* 0.060 0.070 0.061 -0.057 0.108 -0.126 0.135 -0.044 0.036 -0.002 0.068 0.019 0.085 9/23/2009 0.041* 0.024 0.028 0.044 0.069 0.048 0.046 0.028 0.009 0.054 0.042 0.057 0.005 0.052 -0.050 0.095 -0.096 0.117 0.035 0.034 0.058 0.065 0.108 0.082

11/4/2009 0.021 0.028 0.023 0.053 -0.041 0.062 0.039 0.032 0.063 0.063 -0.003 0.069 0.069 0.045 0.135 0.085 0.154 0.105 -0.033 0.036 -0.068 0.071 -0.117 0.086

8/10/2010 0.034** 0.014 -0.013 0.019 0.001 0.024 0.037** 0.019 -0.029 0.021 -0.023 0.026 -0.010 0.051 -0.057 0.088 -0.084 0.107 0.034 0.031 0.037 0.054 0.071 0.067 8/27/2010 -0.139*** 0.015 -0.100*** 0.021 -0.104*** 0.028 -0.112** 0.017 -0.101*** 0.028 -0.118*** 0.032 0.136*** 0.044 0.024 0.076 -0.015 0.092 -0.174*** 0.034 -0.087 0.058 -0.061 0.070

9/21/2010 0.000 0.046 -0.110* 0.059 -0.081 0.077 -0.002 0.048 -0.148** 0.073 -0.123 0.087 -0.065 0.050 -0.114 0.086 -0.123 0.103 0.024 0.045 -0.010 0.081 0.007 0.101

10/15/2010 0.092** 0.044 -0.006 0.070 -0.057 0.083 0.131** 0.043 0.031 0.081 -0.020 0.095 0.073* 0.042 0.106 0.074 0.128 0.087 0.018 0.045 -0.071 0.081 -0.134 0.098 11/3/2010 -0.044 0.035 -0.011 0.061 -0.023 0.074 -0.012 0.040 0.008 0.074 0.017 0.087 0.141*** 0.042 0.057 0.070 0.146* 0.084 -0.099** 0.040 -0.057 0.074 -0.101 0.091

8/9/2011 0.032*** 0.012 -0.045*** 0.016 -0.085*** 0.018 0.002 0.016 -0.112*** 0.027 -0.071** 0.034 -0.101* 0.059 -0.232** 0.104 0.056 0.141 0.099** 0.040 0.114** 0.058 -0.048 0.075

8/26/2011 -0.013 0.018 0.014 0.026 0.018 0.031 -0.027 0.024 0.017 0.042 0.035 0.042 -0.045 0.075 0.012 0.129 0.068 0.126 0.026 0.041 0.020 0.072 -0.003 0.079 9/21/2011 0.016 0.014 0.002 0.019 -0.010 0.022 -0.020 0.017 -0.093*** 0.030 -0.107*** 0.032 -0.133** 0.062 -0.322*** 0.107 -0.318*** 0.115 0.105*** 0.038 0.195*** 0.073 0.183** 0.078

6/20/2012 0.012 0.013 -0.001 0.020 -0.006 0.025 0.012 0.014 0.002 0.026 0.008 0.032 -0.003 0.042 0.018 0.073 0.055 0.094 0.011 0.024 -0.006 0.047 -0.022 0.057

8/22/2012 -0.038** 0.016 -0.093*** 0.022 -0.052** 0.023 -0.051*** 0.019 -0.126*** 0.031 -0.073** 0.035 -0.059 0.038 -0.092 0.067 -0.085 0.083 0.001 0.020 -0.019 0.039 0.010 0.047

8/31/2012 0.017 0.014 0.002 0.021 -0.008 0.021 0.006 0.016 -0.012 0.028 -0.013 0.029 -0.050 0.032 -0.043 0.057 -0.012 0.070 0.040* 0.022 0.024 0.040 -0.001 0.048

9/13/2012 -0.036*** 0.013 -0.063*** 0.019 -0.035* 0.021 -0.029* 0.017 -0.005 0.028 0.017 0.031 0.039 0.033 0.218*** 0.065 0.191** 0.079 -0.068*** 0.022 -0.177*** 0.037 -0.136*** 0.044

12/12/2012 -0.020*** 0.008 -0.006 0.013 0.007 0.017 -0.003 0.011 0.022 0.019 0.029 0.024 0.066** 0.026 0.102** 0.047 0.084 0.058 -0.055*** 0.015 -0.057* 0.030 -0.041 0.036 5/1/2013 -0.008 0.013 0.013 0.022 0.031 0.029 -0.020 0.016 0.004 0.029 0.061* 0.037 -0.033 0.029 -0.028 0.052 0.100 0.062 0.013 0.017 0.019 0.034 -0.032 0.039

5/22/2013 0.054*** 0.013 0.090*** 0.023 0.094*** 0.029 0.076*** 0.021 0.109*** 0.035 0.110*** 0.042 0.061* 0.033 0.038 0.059 0.043 0.065 0.017 0.017 0.057* 0.030 0.056 0.035

6/19/2013 0.028 0.029 0.194*** 0.048 0.284*** 0.062 0.046 0.037 0.242*** 0.062 0.371*** 0.076 0.069** 0.033 0.136** 0.060 0.247*** 0.073 -0.001 0.030 0.084* 0.050 0.100 0.063 12/18/2013 0.009 0.018 0.042 0.034 0.032 0.042 0.016 0.023 0.055 0.044 0.032 0.050 0.025 0.028 0.028 0.052 -0.018 0.063 0.000 0.018 0.024 0.036 0.037 0.044

1/29/2014 -0.002 0.016 -0.013 0.029 -0.037 0.033 -0.018 0.021 -0.021 0.037 -0.040 0.040 -0.045* 0.027 -0.013 0.052 -0.019 0.061 0.019 0.018 -0.008 0.032 -0.019 0.038

3/19/2014 0.036*** 0.014 0.093*** 0.027 0.095*** 0.030 0.044** 0.020 0.106*** 0.036 0.105*** 0.040 0.030 0.030 0.025 0.053 0.013 0.062 0.020 0.021 0.063* 0.036 0.072 0.044 4/30/2014 -0.019 0.029 0.016 0.051 0.160** 0.064 -0.031 0.036 0.000 0.062 0.157** 0.071 -0.017 0.029 -0.041 0.045 -0.055 0.052 -0.002 0.031 0.043 0.060 0.152** 0.072

5/7/2014 -0.021 0.036 -0.100* 0.053 -0.142** 0.056 -0.001 0.037 -0.076 0.060 -0.123* 0.061 0.016 0.029 0.038 0.048 0.092* 0.053 -0.041 0.034 -0.110* 0.060 -0.172** 0.067

6/18/2014 0.007 0.024 0.015 0.037 0.013 0.042 0.004 0.028 0.031 0.044 0.023 0.049 -0.007 0.029 0.053 0.050 0.029 0.056 0.007 0.026 -0.016 0.046 -0.002 0.052 7/15/2014 0.013 0.021 0.002 0.036 0.232*** 0.040 0.019 0.021 0.001 0.041 0.223*** 0.045 0.005 0.029 0.003 0.050 -0.080 0.050 0.003 0.024 0.007 0.045 0.231*** 0.051

7/30/2014 -0.084** 0.034 0.006 0.057 -0.008 0.047 -0.076** 0.037 0.027 0.066 0.006 0.052 0.077*** 0.029 0.056 0.050 0.052 0.052 -0.097** 0.039 -0.027 0.070 -0.031 0.061

8/22/2014 0.007 0.040 0.033 0.069 0.009 0.071 -0.006 0.048 0.012 0.085 0.012 0.092 -0.023 0.030 -0.062 0.050 -0.036 0.057 0.030 0.038 0.045 0.072 0.002 0.074 9/17/2014 0.006 0.033 0.004 0.054 0.056 0.067 0.012 0.039 0.007 0.070 0.054 0.078 0.014 0.032 0.031 0.057 -0.018 0.065 -0.008 0.032 -0.023 0.065 0.041 0.071

10/29/2014 0.011 0.030 -0.007 0.050 0.018 0.061 0.017 0.029 0.002 0.056 0.034 0.070 0.007 0.027 0.021 0.049 0.047 0.059 0.003 0.032 -0.019 0.062 -0.013 0.073





38

Table 7: Cumulative Effects of QE Announcements on the Mortgage Rate and the Treasury Rate

Row 1 reports the cumulative abnormal returns of the 30-year mortgage rate, the 30-year Treasury rate and the

spread between them across all announcements in each round of QE with 1-day, 3-day and 5-day event

windows from my estimation. As a comparison, the cumulative effects on mortgage rates and MBS yields found

in other literature are reported. The cumulative changes of the 30-year MBS yield and the 30-year Treasury rate

are found in Krishnamurthy and Vissing-Jorgensen (2011) and Di Maggio et al. (2016), all other studies report

cumulative changes of the 10-year MBS yield and the 10-year Treasury rate. For comparison purpose, we only

focus on “easing” announcements, four events of decrease in purchases of assets are not used for calculation. In

line with the events used in other studies, four events on 8/27/2010, 11/3/2010, 6/20/2012 and 12/12/201234 are

excluded from my calculation.

Study Event Window

Size

Round of

QE

Mortgage Rate

(Market

Model)

Mortgage Rate

(Constant Mean

Return Model)

Treasury

Rate Spread

My Estimation in this

paper

1-day, 3-day,

5-day

QE1 -7, -38, -37 -23, -63, -56 -54, -76, -58 31, 19, 10

QE2 4, -16, -10 4, -21, -18 -9, -21, -25 9, 4, 12

OT 4, -4, -10 -5, -23, -17 -34, -66, -23 35, 51, 20

QE3 -7, -20, -12 -9, -17, -8 -9, 10, 11 -4, -26, -19

Tapering 5, 49, 107 10, 60, 124 22, 35, 48 -6, 21, 64

Gagnon et al. (2011) 1-day, 2-day QE1 -113, -115 -91, -105

Krishnamurthy and

Vissing-Jorgensen

(2011)

2-day for QE1,

1-day and 2-

day for QE2

QE1 -107 -73

QE2 -9, -8 -9, -21

Christensen and

Rudebusch (2012) 1-day QE1 -100

Neely (2015) 1-day QE1 -100

Altavilla and

Giannone (2017) 2-day

QE1 -104

QE2 -29

OT -43

QE3 -4

Tapering 40

Bowman et al. (2015) 2-day

QE1 -99

QE2 -28

OT -41

QE3 -9

Tapering 37

Borrallo et al. (2016) 2-day

QE1 -104

QE2 -29

OT -37

QE3 -10

Tapering 42

Di Maggio et al.

(2016)

3-month,

6-month

QE1 -117, -140

QE2 -37, -47

OT -46, -63

QE3 -18, -32

Tapering 26, -53

34 Events on 8/27/2010 and 11/3/2010 (both in QE2) are not picked by Krishnamurthy and Vissing-Jorgensen (2011), and Events on

6/20/2012 (in OT) and 12/12/2012 (in QE3) are not picked by Bowman et al. (2015) and Borrallo et al (2016). In fact, as discussed in

Part 5, these four events are also the events either contaminated by Economy report release or are already expected by the market.





39

Table 8: Abnormal Returns (ARs) and Cumulative Abnormal Returns (CARs) for Event Groups

A total of 31 QE events are grouped by type of asset purchased, increase or decrease of purchase, and QE

round. ARs are reported for 1-day window and CARs are reported for 3-day and 5-day window. The ARs used

to calculate CARs are not reported for 3-day and 5-day window cases, but can be found in Appendix, Table

A5.

Panel A: 1-day window (5) (6) (7) (8)


AR SE AR SE AR SE AR SE

QE1_MBS_Increase -0.095*** 0.033 -0.086 0.052 0.004 0.079 -0.130** 0.066

QE1_T_Increase 0.010 0.046 -0.056 0.045 -0.223** 0.100 0.210*** 0.079

QE1_Both_Increase 0.055** 0.024 -0.001 0.024 -0.198*** 0.060 0.164*** 0.057

QE1_MBS_Decrease 0.038* 0.023 0.051** 0.025 0.045 0.041 0.000 0.041

QE1_T_Decrease -0.027 0.032 -0.001 0.039 0.085 0.075 -0.069 0.054

QE2_T_Increase 0.038* 0.021 0.038* 0.023 -0.050 0.044 0.047 0.040

QE2_T_Decrease 0.117** 0.058 0.157*** 0.053 0.087 0.057 0.029 0.073

OT_T_Increase 0.020 0.018 -0.023 0.021 -0.162** 0.077 0.161*** 0.058

OT_Both_Increase 0.013 0.030 -0.014 0.021 -0.094 0.059 0.092* 0.054

QE3_MBS_Increase -0.046*** 0.018 -0.034* 0.020 0.047 0.040 -0.104*** 0.035

QE3_Both_Increase -0.019 0.031 -0.025 0.025 -0.066** 0.031 0.027 0.027

Tapering 0.006 0.010 0.001 0.012 0.008 0.011 0.009 0.011

Panel B: 3-day window (5) (6) (7) (8)



QE1_MBS_Increase -0.116** 0.049 -0.101 0.072 -0.018 0.120 -0.178* 0.103

QE1_T_Increase 0.004 0.067 -0.107 0.081 -0.358** 0.154 0.311*** 0.118

QE1_Both_Increase -0.048 0.045 -0.082* 0.048 -0.268** 0.104 0.082 0.078

QE1_MBS_Decrease 0.024 0.037 0.031 0.045 0.052 0.068 -0.013 0.061

QE1_T_Decrease -0.064 0.042 -0.079 0.056 -0.068 0.120 -0.006 0.079

QE2_T_Increase -0.025 0.023 -0.044* 0.026 -0.109 0.069 0.036 0.056

QE2_T_Decrease -0.006 0.084 0.038 0.092 0.128 0.087 -0.108 0.101

OT_T_Increase 0.004 0.025 -0.109*** 0.036 -0.393*** 0.119 0.299*** 0.086

OT_Both_Increase 0.034 0.034 -0.021 0.051 -0.161* 0.095 0.114* 0.066

QE3_MBS_Increase -0.079*** 0.023 -0.004 0.033 0.264*** 0.072 -0.271*** 0.044

QE3_Both_Increase -0.066 0.046 -0.087** 0.039 -0.085* 0.049 0.004 0.035

Tapering 0.021 0.017 0.016 0.020 0.009 0.017 0.028 0.018

Panel C: 5-day window (5) (6) (7) (8)



QE1_MBS_Increase -0.115* 0.065 -0.050 0.087 0.220 0.154 -0.318*** 0.114

QE1_T_Increase -0.049 0.089 -0.231** 0.108 -0.651*** 0.220 0.505*** 0.161

QE1_Both_Increase -0.035 0.052 -0.035 0.053 -0.263** 0.130 0.066 0.100

QE1_MBS_Decrease 0.010 0.053 0.012 0.054 0.031 0.084 -0.020 0.078

QE1_T_Decrease -0.095* 0.048 -0.144** 0.066 -0.149 0.148 0.027 0.096

QE2_T_Increase -0.005 0.028 -0.035 0.029 -0.132 0.085 0.083 0.069

QE2_T_Decrease -0.066 0.101 -0.022 0.109 0.160 0.105 -0.203* 0.118

OT_T_Increase -0.010 0.029 -0.127*** 0.039 -0.375*** 0.137 0.279*** 0.096

OT_Both_Increase 0.011 0.040 0.000 0.052 -0.005 0.122 0.041 0.099

QE3_MBS_Increase -0.042 0.029 0.024 0.040 0.230*** 0.088 -0.208*** 0.051

QE3_Both_Increase -0.059 0.048 -0.067 0.043 -0.060 0.059 0.002 0.040

Tapering 0.032* 0.019 0.028 0.024 0.000 0.022 0.034 0.022

Note: ***, ** and * indicate statistical significance at 0.01, 0.05, 0.1





40

Figure 2: Cumulative Abnormal Returns (CARs) on Days in a 5-day Window of Grouped Event

The CAR on each day inside a 5-day event window for grouped events are reported in the figure. Each CAR is calculated

as a summation of abnormal returns from two days before the event day (t-2) to the day interested. The region between

two dash lines in each graph is the 95% confidence interval of ARs. t indicates the event day.

Panel A: CARs of 30-year Mortgage Rate (Market Regression)

-0.300

-0.250

-0.200

-0.150

-0.100

-0.050

0.000

0.050

0.100

t-2 t-1 t t+1 t+2

MBS Purchases Increase in QE1

-0.300

-0.200

-0.100

0.000

0.100

0.200

0.300

t-2 t-1 t t+1 t+2

Treasury Purchases Increase in QE1

-0.200

-0.150

-0.100

-0.050

0.000

0.050

0.100

0.150

0.200

t-2 t-1 t t+1 t+2

Both Purchases Increase in QE1

-0.150

-0.100

-0.050

0.000

0.050

0.100

0.150

0.200

t-2 t-1 t t+1 t+2

MBS Purchases Decrease in QE1

-0.250

-0.200

-0.150

-0.100

-0.050

0.000

0.050

0.100

0.150

t-2 t-1 t t+1 t+2

Treasury Purchases Decrease in QE1

-0.150

-0.100

-0.050

0.000

0.050

0.100

t-2 t-1 t t+1 t+2


-0.300

-0.200

-0.100

0.000

0.100

0.200

t-2 t-1 t t+1 t+2


-0.080

-0.060

-0.040

-0.020

0.000

0.020

0.040

0.060

0.080

t-2 t-1 t t+1 t+2

Treasury Purchases Increase in OT

-0.100

-0.050

0.000

0.050

0.100

0.150

t-2 t-1 t t+1 t+2

Both Purchases Increase in OT

-0.150

-0.100

-0.050

0.000

0.050

0.100

t-2 t-1 t t+1 t+2

MBS Purchses Increase in QE3

-0.200

-0.150

-0.100

-0.050

0.000

0.050

0.100

t-2 t-1 t t+1 t+2


-0.040

-0.020

0.000

0.020

0.040

0.060

0.080

t-2 t-1 t t+1 t+2

QE Tapering





41

Panel B: CARs of 30-year Mortgage Rate (Constant Mean Return Regression)

-0.300-0.250-0.200-0.150-0.100-0.0500.0000.0500.1000.150

t-2 t-1 t t+1 t+2


-0.500

-0.400

-0.300

-0.200

-0.100

0.000

0.100

0.200

t-2 t-1 t t+1 t+2


-0.200

-0.150

-0.100

-0.050

0.000

0.050

0.100

0.150

t-2 t-1 t t+1 t+2


-0.150

-0.100

-0.050

0.000

0.050

0.100

0.150

0.200

t-2 t-1 t t+1 t+2


-0.300

-0.200

-0.100

0.000

0.100

0.200

t-2 t-1 t t+1 t+2


-0.140-0.120-0.100-0.080-0.060-0.040-0.0200.0000.0200.040

t-2 t-1 t t+1 t+2


-0.300

-0.200

-0.100

0.000

0.100

0.200

0.300

t-2 t-1 t t+1 t+2


-0.250

-0.200

-0.150

-0.100

-0.050

0.000

0.050

t-2 t-1 t t+1 t+2


-0.150

-0.100

-0.050

0.000

0.050

0.100

0.150

t-2 t-1 t t+1 t+2


-0.100

-0.050

0.000

0.050

0.100

0.150

t-2 t-1 t t+1 t+2


-0.200

-0.150

-0.100

-0.050

0.000

0.050

0.100

t-2 t-1 t t+1 t+2


-0.040

-0.020

0.000

0.020

0.040

0.060

0.080

0.100

t-2 t-1 t t+1 t+2

QE Tapering





42

Panel C: CARs of 30-year Treasury Rate

-0.300-0.200-0.1000.0000.1000.2000.3000.4000.5000.600

t-2 t-1 t t+1 t+2


-1.200

-1.000

-0.800

-0.600

-0.400

-0.200

0.000

t-2 t-1 t t+1 t+2


-0.600

-0.400

-0.200

0.000

0.200

0.400

t-2 t-1 t t+1 t+2


-0.200-0.150-0.100-0.0500.0000.0500.1000.1500.2000.250

t-2 t-1 t t+1 t+2


-0.500

-0.400

-0.300

-0.200

-0.100

0.000

0.100

0.200

0.300

t-2 t-1 t t+1 t+2


-0.400

-0.300

-0.200

-0.100

0.000

0.100

t-2 t-1 t t+1 t+2


-0.100

0.000

0.100

0.200

0.300

0.400

0.500

t-2 t-1 t t+1 t+2


-0.800

-0.600

-0.400

-0.200

0.000

0.200

t-2 t-1 t t+1 t+2


-0.300

-0.200

-0.100

0.000

0.100

0.200

0.300

t-2 t-1 t t+1 t+2


-0.100

0.000

0.100

0.200

0.300

0.400

0.500

t-2 t-1 t t+1 t+2


-0.200

-0.150

-0.100

-0.050

0.000

0.050

0.100

t-2 t-1 t t+1 t+2


-0.060

-0.040

-0.020

0.000

0.020

0.040

0.060

0.080

t-2 t-1 t t+1 t+2

QE Tapering





43

Panel D: CARs of Spread between 30-year Mortgage Rate and Treasury Rate

-0.600

-0.500

-0.400

-0.300

-0.200

-0.100

0.000

0.100

0.200

t-2 t-1 t t+1 t+2


0.000

0.200

0.400

0.600

0.800

1.000

t-2 t-1 t t+1 t+2


-0.300

-0.200

-0.100

0.000

0.100

0.200

0.300

0.400

t-2 t-1 t t+1 t+2


-0.200

-0.150

-0.100

-0.050

0.000

0.050

0.100

0.150

0.200

t-2 t-1 t t+1 t+2


-0.200

-0.100

0.000

0.100

0.200

0.300

t-2 t-1 t t+1 t+2


-0.200-0.150-0.100-0.0500.0000.0500.1000.1500.2000.250

t-2 t-1 t t+1 t+2


-0.500

-0.400

-0.300

-0.200

-0.100

0.000

0.100

t-2 t-1 t t+1 t+2


-0.100

0.000

0.100

0.200

0.300

0.400

0.500

0.600

t-2 t-1 t t+1 t+2


-0.200

-0.100

0.000

0.100

0.200

0.300

t-2 t-1 t t+1 t+2


-0.400

-0.300

-0.200

-0.100

0.000

0.100

t-2 t-1 t t+1 t+2


-0.100

-0.050

0.000

0.050

0.100

0.150

t-2 t-1 t t+1 t+2


-0.060

-0.040

-0.020

0.000

0.020

0.040

0.060

0.080

0.100

t-2 t-1 t t+1 t+2

QE Tapering





44

Table 9: Coefficient Estimates of Additional Control Variables in Regressions

The change of log unexpected CPI, the change of log unexpected Case-Shiller House Price Index (HPI), the

change of unexpected unemployment rate (UER), and the lagged variables (2 lags for each of these 3 variables)

are added to each of the 4 models as independent variables. This table only reports the estimates and standard

errors of coefficients associated with these newly added variables by using 1-day event window. ARs, CARs and

coefficient estimates of other independent variables are not reported in this table since the values and significances

of them are similar to the case without including these control variables.

Dependent Variable

_ 30tMR _ 30tMR _ 30tT tSpread

Control Variables Estimate SE Estimate SE Estimate SE Estimate SE

ln( )tCPI -0.008 0.014 -0.012 0.015 0.013 0.026 -0.021 0.021

1ln( )tCPI − 0.013 0.012 0.019 0.014 0.026 0.024 0.033 0.021

2ln( )tCPI − -0.003 0.012 0.006 0.014 0.014 0.022 0.011 0.022

ln( )tHPI -0.009 0.011 -0.012 0.012 -0.018 0.018 0.005 0.018

1ln( )tHPI − -0.016 0.011 -0.022 0.014 0.013 0.021 -0.050** 0.020

2ln( )tHPI − 0.010 0.012 0.006 0.012 0.006 0.018 0.021 0.017

tUER -0.028 0.020 -0.020 0.027 0.026 0.032 -0.010 0.032

1tUER − 0.009 0.018 0.024 0.020 -0.019 0.031 0.022 0.030

2tUER − 0.031* 0.018 0.026 0.021 0.019 0.031 0.016 0.030






45

Table 10: Differences in Regression Results by Using 10-year Treasury Rate Instead of 30-year Treasury

Rate

Values and standard deviations (SEs) for CAR of mortgage rate from regressions controlling for 10-year Treasury

rate and 30-year Treasury rate respectively are reported.

Mortgage Rate

(Controlling for 10-year

Treasury Rate)

Mortgage Rate

(Controlling for 30-year

Treasury Rate)

CAR SE CAR SE

QE1_MBS_Increase -0.124** 0.061 -0.115* 0.065

QE1_T_Increase -0.024 0.082 -0.049 0.089

QE1_Both_Increase -0.038 0.051 -0.035 0.052

QE1_MBS_Decrease 0.010 0.047 0.010 0.053

QE1_T_Decrease -0.044 0.042 -0.095* 0.048

QE2_T_Increase 0.032 0.026 -0.005 0.028

QE2_T_Decrease -0.059 0.094 -0.066 0.101

OT_T_Increase -0.063** 0.027 -0.010 0.029

OT_Both_Increase -0.011 0.040 0.011 0.040

QE3_MBS_Increase -0.046* 0.027 -0.042 0.029

QE3_Both_Increase -0.048 0.035 -0.059 0.048

Tapering 0.022 0.017 0.032* 0.019






the effects of quantitative easing announcements on the

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